This tab displays a Circos diagram, a circular plot showing each
chromosome of the genome as a segment, with each datatype shown as a
separate track on the image. Move your mouse over the plot to
highlight a region of the genome, then click to view the region in
the Genome Browser.
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Circos gives a clear overview of all the structural variants,
aberrant gene expression and copy number, and both coding and
non-coding point mutations for the selected sample. This image is
available for whole genome screens.

The genome browser shows COSMIC annotations for this gene in a genomic context.
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Note: in some web browsers the genome browser
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This tab shows the mutations observed in this sample. See more information in our
help pages.

Gene

Transcript

Census Tier 1

AA Mutation

CDS Mutation

Somatic status

Zygosity

Validated

Type

Position

Mutation Filters

Mutation Impact

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The mutation impact filters introduced in COSMIC v73 have been derived from the new FATHMM-MKL algorithm. This algorithm predicts the functional, molecular and phenotypic consequences of protein missense variants using hidden Markov models.

The new method improves on the older version of FATHMM and now incorporates ENCODE annotation for its prediction. This method is as powerful as CADD scores for coding variants and shows improved prediction for non-coding variants (compared to GWAVA and CADD).

The functional scores for individual mutations from FATHMM-MKL are in the form of a single p-value, ranging from 0 to 1. Scores above 0.5 are deleterious, but in order to highlight the most significant data in COSMIC, only scores ≥ 0.7 are classified as 'Pathogenic'. Mutations are classed as 'Neutral' if the score is ≤ 0.5. In addition, each functional score is classified into 10 groups of features, depending on whether it is a coding or non-coding variant. Please see the original publication for more details regarding the feature classification (doi:10.1093/bioinformatics/btv009).

The following is reproduced from the publication in order to aid interpretation:

Description for each of the feature groups [A-J]

A. 46-Way Sequence Conservation: based on multiple sequence alignment scores, at the nucleotide level, of 46 vertebrate genomes compared with the human genome.

I. Genome Segmentation: based on genome-segmentation states using a consensus merge of segmentations produced by the ChromHMM and Segway software.

J. Footprints: based on annotations describing DNA footprints across cell types from ENCODE.

Please note: The current FATHMM-MKL algorithm is trained on the human gene mutation database (The HGMD database http://www.hgmd.cf.ac.uk/ac/index.php), which now also contains somatic variants. Results from the current available version of FATHMM-MKL can be used/has been used for somatic variants, but the user should be aware of the caveats.
The cancer specific version of FATHMM-MKL is under development and when available these scores will be updated.

Pathogenic

Neutral

Genes

All

Cancer Gene Census

Recurrence

Recurrence is defined by counting whole genome screened tumour samples, according to the mutation type -

Substitutions: ≥ 3 samples with a missense substitution in the same codonInframe Indels: ≥ 3 samples with an inframe indel in the same codon Terminations: > 10 samples with a mutation causing premature protein termination

This tab shows the CNVs (copy number variants) for this sample. Only variants (classified as gain or loss) are listed.
[more details]

Table Information

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The average ploidy of the genome is . The table currently shows only high value (numeric) copy number data. Copy number segments are excluded if the total copy number and minor allele values are unknown.

Click here to include all copy number data. For more detailed information about copy number data and gain/loss definitions click here.

Gene

Expression

Expr Level (Z-Score)

Over Expressed; Z-Score > 2.0

Under Expressed; Z-Score < -2.0

Normal; Z-Score within the range -2.0 to 2.0

CN Type

Minor Allele

Copy Number

CN Segment Posn.

Average Ploidy

1. N/A represents cases where the average ploidy value is not available( mostly ICGC samples). For some TCGA samples where the minor allele information is not available the average ploidy value could not be calculated.

2. For TCGA samples, the ASCAT algorithm was used to calculate the average ploidy.

3. For CGP samples, the PICNIC algorithm was used to calculate the average ploidy.